Signal noise reduction for imaging in biological analysis

ABSTRACT

A system and method for characterizing contributions to signal noise associated with charge-coupled devices adapted for use in biological analysis. Dark current contribution, readout offset contribution, photo response non-uniformity, and spurious charge contribution can be determined by the methods of the present teachings and used for signal correction by systems of the present teachings.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a Continuation Application of U.S. patentapplication Ser. No. 12/220,763, filed Jul. 28, 2008, now U.S. Pat. No.7,715,004, which is a Continuation Application of U.S. patentapplication Ser. No. 11/803,403, filed May 14, 2007, now U.S. Pat. No.7,405,823 B2, which is a Continuation Application of U.S. patentapplication Ser. No. 10/913,601, filed Aug. 5, 2004, now U.S. Pat. No.7,233,393 B2.

FIELD

The present teachings generally relate to the field of signal processingand more particularly, to a system and methods for characterizing andcorrecting for noise contributions associated with signal imaging inbiological analysis.

INTRODUCTION

During biological analysis, such as nucleotide sequencing, microarrayprocessing, sequence detection, or high-throughput screening,photo-detectors such as charge coupled devices (CCD) can be used todetect signals arising from labeled samples or probe features responsiveto selected target analytes. These signals can take the form offluorescent or visible light emissions that are desirably analyzed toquantify signal intensities arising from each labeled sample or probefeature and are subsequently resolved to quantitatively or qualitativelyevaluate the presence of a target analyte within a sample.

Generally, a CCD used in such a biological analysis includes an array ofsignal detecting pixels. The signal detection for a given pixel can becharacterized as a conversion of an incident electromagnetic energysignal into a number of electron-hole pairs. The pixel can be configuredto collect either the electrons or the holes thus generated with thenumber of collected charges representative of the incident energy. A CCDhaving a plurality of such pixels with collected charges can be read outby a sequence of shifting operations by applying a sequence of gatevoltages to the pixels in a predetermined manner. The charge collectedfrom a selected pixel can then be read out or quantitated and used forfurther analysis.

The operation of the CCD in the foregoing manner results in severalundesirable effects that can be referred to collectively as “signalnoise.” Noise can include various contributions, and if not accountedfor, generally degrades the quality of signal acquisition and candetrimentally affect the biological analysis. Consequently, there is anongoing need for an improved approach to signal acquisition by photodetectors used in biological analysis systems.

SUMMARY

In various embodiments, the present teachings can provide a system fordetecting one or more identifiable signals associated with one or morebiological samples, the system including a segmented detector includinga plurality of pixels that are capable of forming an optical image offluorescent light emitted from the biological samples, a readoutcomponent that is capable of reading an output signal from each pixel,wherein the output signal includes a charge collected and transferredfrom the pixel, and wherein the readout component includes an outputregister that receives transferred charges from the plurality of pixelsfor readout, a controller that is capable of correcting signal noisefrom the output signal, wherein signal noise includes a dark currentcontribution and a readout offset contribution, and a processor capableof determining the dark current contribution and the readout offsetcontribution.

In various embodiments, the present teachings can provide a method forreducing signal noise from an array of pixels of a segmented detectorfor biological samples, wherein the signal noise includes a dark currentcontribution and readout offset contribution, the method includingproviding a substantially dark condition for the array of pixels,wherein the dark condition includes being substantially free offluorescent light emitted from the biological samples, providing a firstoutput signal from a binned portion of the array of pixels by collectingcharge for a first exposure duration, transferring the collected chargeto an output register and reading out the register, wherein transferringof the collected charge from the binned pixels includes providing a gatevoltage to a region near the binned pixels to move the collected chargefrom the binned pixels, and wherein the collected charge is transferredin a manner that causes the collected charge to be shifted to the outputregister, providing a second output signal from each pixel by collectingcharge for a second exposure duration, transferring the collected chargeto the output register, and reading out the register, providing a thirdoutput signal by resetting and reading out the output register,determining the dark current contribution and the readout offsetcontribution from the first output signal, the second output signal, andthe third output signal.

In various embodiments, the present teachings can provide a method ofcharacterizing signal noise associated with operation of acharge-coupled device (CCD) adapted for analysis of biological samples,wherein the signal noise includes a dark current contribution, readoutoffset contribution, and spurious change contribution, the methodincluding providing a plurality of first data points associated withfirst outputs provided from the CCD under a substantially dark conditionduring a first exposure duration, providing a plurality of second datapoints associated with second outputs provided from the CCD under thesubstantially dark condition during a second exposure duration whereinthe second duration is different from the first duration, providing aplurality of third data points associated with third outputs providedfrom a cleared output register of the CCD without having chargetransferred thereto, determining the dark current contribution per unitexposure time by comparing the first data points and the second datapoints, determining the readout offset contribution from the third datapoints, and determining the spurious charge contribution based on thedark current contribution and the readout offset contribution.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a functional block diagram of a system adapted tomeasure components associated with biological related processesaccording to the present teachings;

FIGS. 1B and 1C illustrate example biological analysis systems thatutilize CCDs to detect signals from samples adapted to emitelectromagnetic energy in a selected manner according to the presentteachings;

FIG. 2 illustrates one embodiment of a CCD readout control systemaccording to the present teachings;

FIGS. 3A and 3B illustrate one embodiment of charge collection andcharge transfer configurations for a pixel according to the presentteachings;

FIG. 4 illustrates one embodiment of a pixel array read out by pixelcharge shifting according to the present teachings;

FIG. 5 illustrates one embodiment of a readout control system thatallows characterization of various contributions of noise associatedwith the operation of the pixels according to the present teachings;

FIGS. 6A and 6B illustrate a method for determining a dark currentcontribution of noise associated with the operation of the pixelsaccording to the present teachings;

FIGS. 7A and 7B illustrate a method for determining a readout offsetcontribution of noise associated with the operation of the pixelsaccording to the present teachings;

FIGS. 8A and 8B illustrate a method for determining a spurious chargecontribution of the noise associated with the operation of the pixelsaccording to the present teachings;

FIGS. 9A to 9D illustrate another method for determining the variousnoise contributions associated with the operation of the pixelsaccording to the present teachings;

FIGS. 10A and 10B illustrate a method for characterizing the spuriouscharge production as a function of a gate voltage profile applied to thepixel to shift out the collected charge according to the presentteachings;

FIGS. 11A-11C illustrate an application of the pixels' noisecontributions characterization to identify hot pixels according to thepresent teachings;

FIGS. 12A-12C illustrate an application of the pixels' noisecontributions characterization to identify cold or dead pixels accordingto the present teachings;

FIGS. 13A-13C illustrate an application of the pixels' noisecontributions characterization to normalize the responses of the pixelsaccording to the present teachings; and

FIG. 14 illustrates an application of the pixels' noise contributionscharacterization to correct a raw signal output from a CCD according tothe present teachings.

DESCRIPTION OF VARIOUS EMBODIMENTS

In this application, the use of the singular includes the plural unlessspecifically stated otherwise. In this application, the use of “or”means “and/or” unless stated otherwise. Furthermore, the use of the term“including”, as well as other forms, such as “includes” and “included”,is not limiting. Also, terms such as “element” or “component” encompassboth elements and components having one unit and elements and componentsthat having more than one subunit unless specifically stated otherwise.Wherever possible, the same reference numbers will be used throughoutthe drawings to refer to the same or like parts.

The section headings used herein are for organizational purposes only,and are not to be construed as limiting the subject matter described.All documents cited in this application, including, but not limited topatents, patent applications, articles, books, and treatises, areexpressly incorporated by reference in their entirety for any purpose.In the event that one or more of the incorporated literature and similarmaterials differs from or contradicts this application, including butnot limited to defined terms, term usage, described techniques, or thelike, this application controls.

The term “fluorescent” as used herein refers to light emitted by abiological sample whether by fluorescence or chemiluminescence.

The term “biological sample” or “biological analysis” as used hereinrefers to a material and processes related to nucleic acids as known inthe biological arts.

FIG. 1A illustrates an exemplary schematic diagram for a biologicalanalyzer 100 capable of sequence determination or fragment analysis fornucleic acid samples and other applications. In various embodiments, theanalyzer 100 can include one or more components or devices that are usedfor labeling and identification of the sample and can perform automatedsequence analysis. The various components of the controller can includeseparate components or a singular integrated system. It will beappreciated that the present teachings can be applied to both automaticand semi-automatic sequence analysis systems as well as to methodologieswherein some of the sequence analysis operations are manually performed.Additionally, the methods described herein can be applied to otherbiological analysis platforms to improve the overall quality of theanalysis.

In various embodiments, the methods and systems of the present teachingscan be applied to numerous different types and classes of photo andsignal detection methodologies and are not necessarily limited toCCD-based detectors. The present teachings describe various embodimentsfor sequence analysis and for other biological analysis where signalnoise reduction can provide detection of smaller quantities offluorescent light emitted from locations that are in closer proximity.

It will also be appreciated that the methods and systems of the presentteachings can be applied to photo-detectors in these applications.Photo-detectors in general convert incident photons to electricalsignals, and can include, by way example, CCDs, CMOS devices,photomultipliers, or other semiconductor based devices such asphoto-diodes.

In the context of sequence analysis, the exemplary sequence analyzer 100can include a reaction component 102 wherein amplification or reactionsequencing (for example, through label or marker incorporation bypolymerase chain reaction) of various constituent molecules contained inthe sample is performed. Using these amplification techniques, a labelor tag, such as a fluorescent or radioactive dideoxy-nucleotide can beintroduced into the sample constituents resulting in the production of acollection of nucleotide fragments of varying sequence lengths.Additionally, one or more labels or tags can be used during theamplification step to generate distinguishable fragment populations foreach base/nucleotide to be subsequently identified. Followingamplification, the labeled fragments can then be subjected to aseparation operation using a separation component 104. In one aspect,the separation component 104 includes a gel-based or capillaryelectrophoresis apparatus which resolves the fragments intosubstantially discrete populations. Using this approach, electricalcurrent can be passed through the labeled sample fragments which havebeen loaded into a separation matrix (e.g. polyacrylamide or agarosegel). The application of an electrical current results in the migrationof the sample through the matrix. As the sample migration progresses,the labeled fragments are separated and passed through a detector 106wherein resolution of the labeled fragments is performed.

In one aspect, the detector 106 can identify various sizes ordifferential compositions for the fragments based on the presence of theincorporated label or tag. In one exemplary embodiment, fragmentdetection can be performed by generation of a detectable signal producedby a fluorescent label that is excited by a laser tuned to the label'sabsorption wavelength. Energy absorbed by the label results in afluorescence emission that corresponds to a signal measured for eachfragment. By keeping track of the order of fluorescent signal appearancealong with the type of label incorporated into the fragment, thesequence of the sample can be discerned. A more detailed explanation ofthe sequencing process is provided in commonly assigned U.S. Pat. No.6,040,586, entitled “Method and System for Velocity-NormalizedPosition-Based Scanning.”

FIG. 1B illustrates exemplary components for a detector 130 which can beused to acquire the signal associated with a plurality of labeledfragments 110. As previously indicated, the labeled fragments 110 can beresolved by measuring the quantity of fluorescence or emitted energygenerated when the fragments 110 are subjected to an excitation 114 ofthe appropriate wavelength and energy that can be provided by a sourcesuch as an LED or tuned laser. The energy emissions 120 produced by alabel 116 associated with the fragments 110 can be detected using acharge-coupled device (CCD) 122 as the fragments 110 pass through adetection window 126 wherein a plurality of energy detecting elements(e.g., pixels) 124 capture at least a portion of the emitted energy fromthe label 116. In one aspect, an electronic signal is generated by theCCD 122 that is approximately proportional to the relative abundance ofthe fragments 110 passing through the detection window 126 at the timeof energy capture and the order which the fragments 110 appear in thedetection window 126 can be indicative of their relative length withrespect to one another based on certain sequencing or fragment analysisschemes.

In various embodiments, readout component 128 can provide electronicsassembly configured to perform readout operations to acquire theelectronic signal generated by the CCD 122 in response to the fragments110. In various embodiments, some of the information that can bedetermined through signal readout and subsequent resolution and peakidentification can include determination of the relative abundance orquantity of each fragment population. Evaluation of the signals canfurther be used to determine the sequence or composition of the sampleusing various known base sequence resolution techniques. It will furtherbe appreciated by one of skill in the art that the exemplified signaldistribution can represent one or more nucleic acid fragments for whichthe relative abundance of each fragment can be evaluated based, in part,upon the determination of the relative area of an associated peak in thesignal distribution. The present teachings can therefore be integratedinto existing analysis approaches to facilitate peak evaluation andsubsequent integration operations typically associated with sequenceanalysis.

In various embodiments, the readout of the signal from the CCD 122 andselected control of the CCD 122 can be advantageously performed bycontroller 132. The controller 132 can be configured to operate inconjunction with one or more processors and/or one or more othercontrollers. Such controller and processor's components can include, butare not limited to, software or hardware components, modules such assoftware modules, object-oriented software components, class componentsand task components, processes methods, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables. Furthermore, the controller 132 can output a processedsignal or analysis results to other devices or instrumentation wherefurther processing can take place.

FIG. 1C illustrates another configuration of exemplary components for adetector 150 which can be used to acquire the signals associated with aplurality of labeled fragments forming an array, microarray, or biochipassay. One exemplary configuration of an array used in biologicalanalysis can include a plurality of labeled fragments configured toadhere selectively to an array of tips 144 of a plurality of fibers 142.Such an array type of sample platform 140 can be utilized tosimultaneously characterize concentrations of different types offragments present in a sample. As previously indicated, the labeledfragments attached to the fiber tips 144 can be resolved by measuringthe quantity of fluorescence or emitted energy generated when thefragments are subjected to an excitation source of the appropriatewavelength and energy from an excitation light source. The energyemissions 146 produced by a label associated with the fragments can bedetected using a charge-coupled device (CCD) 152 via some form of optics156, wherein a plurality of energy detecting elements (e.g., pixels) 154capture at least a portion of the emitted energy from the labeledfragments. In one aspect, an electronic signal is generated by the CCD152 that is approximately proportional to the relative abundance of thefragments in the sample being measured.

In various embodiments, readout component 158 can provide electronicsassembly configured to perform readout operations to acquire theelectronic signal generated by the CCD 152 in response to the fragments.In various embodiments, some of the information that can be determinedthrough signal readout and subsequent resolution and peak identificationcan include determination of the relative abundance or quantity of eachfragment population. The spatial resolution of the detected signalallows determination of the position on the sample platform from whichthe signal was emitted. Thus, by identifying the type of a fiberassociated with that position, one can determine the type of fragmentsattached thereto. Such information facilitates determination of thesequence or composition of the sample using various known base sequenceresolution techniques. It will further be appreciated by one of skill inthe art that the exemplified signal distribution can represent one ormore nucleic acid fragments for which the relative abundance of eachfragment can be evaluated based, in part, upon the determination of therelative area of an associated peak in the signal distribution. Thepresent teachings can therefore be integrated into existing analysisapproaches to facilitate peak evaluation and subsequent integrationoperations typically associated with sequence analysis. It will also beunderstood that similar techniques can be implemented in other types ofanalysis.

In various embodiments, the readout of the signal from the CCD 152 andselected control of the CCD 152 can be advantageously performed by acontroller 160. The controller 160 can be configured to operate inconjunction with one or more processors and/or one or more othercontrollers. Such controller and processor's components can include, butare not limited to, software or hardware components, modules such assoftware modules, object-oriented software components, class componentsand task components, processes methods, functions, attributes,procedures, subroutines, segments of program code, scripts, drivers,firmware, microcode, circuitry, data, databases, data structures,tables, arrays, and variables. Furthermore, the controller 160 canoutput a processed signal or analysis results to other devices orinstrumentation where further processing can take place.

In various embodiments, some of the information that can be determinedthrough signal (from feature) resolution and peak identification caninclude determination of the relative abundance or quantity of eachfragment population. Evaluation of the signals can further be used todetermine the sequence or composition of the sample using various knownbase sequence resolution techniques. It will further be appreciated byone of skill in the art that the exemplified signal distribution canrepresent one or more nucleic acid fragments for which the relativeabundance of each fragment can be evaluated based, in part, upon thedetermination of the relative area of an associated peak in the signaldistribution. The present teachings can therefore be integrated intoexisting analysis approaches to facilitate peak evaluation andsubsequent integration operations typically associated with sequenceanalysis. It will also be understood that similar techniques can beimplemented in other types of biological analysis.

FIG. 2 now illustrates one embodiment of a detector control system 176configured to facilitate the exemplary analysis systems described above.The detector control system 176 includes a controller 170 functionallycoupled to a detector 172 and/or a readout electronic assembly 174. Thedetector control system 176 can be implemented in the exemplarybiological analysis systems of FIGS. 1B and 1C, or any other suitablesystem. Thus, the detector 172 can represent the exemplary CCDs 122 and152 (FIGS. 1B and 1C). Similarly, the readout assembly 174 can representthe exemplary readout electronics 128 and 158.

In one aspect, the present teachings relate to the controllermanipulating the CCD 172 and/or the readout 174 in selected manners tocharacterize various components of a CCD signal that are commonlyreferred to as “signal noise.” By identifying the noise associated withthe CCD signal, it can be reduced in subsequent processing and signalresolution, thereby improving the CCD signal's signal-to-noise (S/N)ratio. In various embodiments, such improvements in the S/N ratio canfacilitate more precise measurement of signals during biologicalanalysis. For example, increasing the S/N ratio can facilitate detectionand resolution of a fainter signal from a fluorescing DNA fragment typethat is present in a relatively low concentrations or abundance.

In some embodiments, such manipulation of the CCD and/or readout can beachieved by controlling voltages associated. The voltages can be changedimage to image. The voltages can also be changed within an image totrade off one or more performance parameters to reduce noise. Forexample, the readout speed can be slowed for an area of the detectorwhere there is a region of interest.

As previously described in context of the exemplary sample analysissystems of FIGS. 1B and 1C, it will be appreciated that the controller170 can be configured to operate in conjunction with one or moreprocessors and/or one or more other controllers. Such controller andprocessor's components can include, but are not limited to, software orhardware components, modules such as software modules, object-orientedsoftware components, class components and task components, processesmethods, functions, attributes, procedures, subroutines, segments ofprogram code, drivers, firmware, microcode, circuitry, data, databases,data structures, tables, arrays, and variables. Furthermore, thecontroller 170 can output a processed signal or analysis results toother devices or instrumentation where further processing can takeplace.

FIGS. 3A and 3B illustrate an exemplary operation of an exemplary CCDpixel. Such a pixel can be used in the CCD 172 described above inreference to FIG. 2. It will be appreciated that the depicted operationof the CCD is exemplary only, and the concepts disclosed herein can beimplemented on other functional and/or operational types of CCDs withoutdeparting from the spirit of the present teachings.

As shown in FIGS. 3A and 3B, the exemplary pixel includes a substrate182 and a storage region 192. An oxide layer 184 separates the substrate182 from a plurality of gates 186 a-186 c that facilitate application ofgate voltages to their corresponding regions in the substrate 182 andthe storage region 192.

FIG. 3A depicts an exemplary storage configuration 180 that can beachieved when the gate voltages V1, V2, and V3 are selected such that apotential profile 190 forms a well 204. In certain embodiments, the gatevoltages V1-V3 are held at a same level, and the presence of the storageregion 192 forms the potential well 204. Charge 194 generated from theimpinging signal(s) are thus collected in the well 204 until they areready to be transferred out. It will be appreciated that for the purposeof description, the “charge” can represent electrons or holes. Thesubstrate 182 and the storage region 192 can be configured in any numberof ways by using various known techniques.

FIG. 3B illustrates an exemplary charge transfer configuration 200wherein voltage V3 on the gate 186 c is applied such that the potentialprofile 190 includes an inverted region 206 substantially adjacent thewell. The potential of the inverted region 206 promotes the charge 194to transfer from the well 192, depicted as being “lower” in theexemplary potential profile 190. The manipulation of the potentialprofile 190 thus allows the collected charge 194 to “shift” from theregion adjacent the gate 186 b to the region adjacent the gate 186 c.Collected charge from the adjacent pixel (not shown) can also have beenshifted in a similar manner, thereby allowing the charge 194 totemporarily occupy the adjacent pixel in the transfer process.

Such transfer of charges from a two dimensional exemplary array 210 ofpixels is illustrated in FIG. 4. The exemplary array 210 is depicted asa 4×4 array having a pixel array 220 and an output register 222. Theexemplary pixel array 220 includes four rows of four pixels 212 a-212 d,214 a-214 d, 216 a-216 d, and 218 a-218 d. The exemplary output register222 includes one row of four register elements 224 a-224 d.

During an exemplary readout operation, the charges stored in the pixelsare shifted vertically (shown in the depiction of FIG. 4) such that row218 charges are shifted to the output row 224 (as depicted by an arrow228 d), row 216 charges are shifted to the row 218 pixels (as depictedby an arrow 228 c), and so on. When the charges from row 218 are shiftedinto the output row 224, they can read out from the readout register 222by being shifted horizontally (shown in the depiction of FIG. 4). Thereadout of the output register 222 is depicted as an arrow 226. Thus, bytransferring the collected charges in the pixels in a series oforchestrated shifts, the magnitudes of the charges originally collectedin their corresponding pixels can be generally preserved and determinedduring the readout process.

As is understood in the art, the process of collecting charge in a givenpixel, reading out of the charge from the pixel in the foregoing manner,and subsequent processing of the read out charge introduces at leastsome “noise” to the collected charge representative of the impingingsignal that in turn is representative of the biological sample beingmeasured. The noise can include different contributions, including butnot limited to, a dark current contribution, spurious chargecontribution, and readout offset contribution.

Dark current generally includes spontaneously generated charge, (forexample, arising from thermal electrons) while the pixel is beingoperated (including integration or exposure operations). The darkcurrent is generated whether or not the pixel is subjected to light, andis generally proportional to the duration of the integration (exposure).

In some embodiments, the amount of dark current can vary across thearray if the readout time is large relative to the integration time.Thus, being able to manipulate different parts of the array can increasethe overall performance of the detector.

Spurious charge can arise during the application of the gate voltage.This effect can be manifested particularly near the edges of the gate,and some of such generated charge can migrate and become part of thecollected charge being transferred. Thus, spurious charge can contributeto the noise during the shifting operation described above in referenceto FIGS. 3B and 4. The magnitude of the spurious charge can furtherdepend on the gate voltage magnitude and/or its duration. In someembodiments, such magnitude depends particularly on whether the pixel ispart of an electron multiplying register.

Readout offset generally results during the processing of the chargeread out from the pixels. As an example, conversion of the analog(charge) signal to a digital representation via an ADC(analog-to-digital converter) typically introduces an offset. Similar tothe dark current, such an offset is present whether or not the pixel issubjected to light.

As illustrated in FIG. 5, one aspect of the present teachings relates tothe controller 170 configured to interact with the CCD 172 and/or thereadout 174 to characterize various contributions 230 of noise thataffect the signal representative of the biological sample beingmeasured. As previously described, the contributions 230 of the noisecan include the dark current, spurious charge, and readout offset.

FIGS. 6A and 6B illustrate one possible manner of determining the darkcurrent contribution associated with a given pixel. A process 240 thatdetermines the dark current can be performed for each of the pluralityof pixels of the CCD. The process 240 begins at a start state 242, andin step 244 that follows, the process 240 induces a dark condition to beapplied to the detector. In step 246 that follows, the process 240provides two or more exposures with different durations from the pixel.For the purpose of description, two such exposures are used. It will beappreciated, however, that more than two exposures can be used withoutdeparting from the spirit of the present teachings. In step 250 thatfollows, the process 240 provides a relationship between the pixeloutput and the exposure duration. Such a relationship can be providedfrom the two exposures in a manner described below in reference to FIG.6B. In step 252 that follows, the process 240 determines the pixel'sdark current per unit exposure time. The process 240 ends at a stopstate 254.

In some embodiments, the dark current can also be determined from amasked area of a device. A number of exposures can be provided andaveraged to improve the signal-to-noise ratio in determining the darkcurrent.

FIG. 6B illustrates a graphical representation 260 of a relationshipbetween the pixel output and the exposure duration (time). A first datapoint 262 a corresponds to a first pixel output D1 provided from a darkexposure lasting for a first duration t1, and a second data point 262 bcorresponds to a second pixel output D2 provided from a dark exposurelasting for a second duration t2.

In one implementation of the process 240 described above, therelationship between the pixel output and the exposure duration isprovided by extrapolating a linear relationship 264 between the firstand second data points 262 a and 262 b. Because the dark current isproportional to the exposure time, the dark current per unit exposuretime (ΔD/Δt) can be approximated from the slope of the linear curve 264.That is,

$\begin{matrix}{\frac{\Delta\; D}{\Delta\; t} = {\frac{{D\; 2} - {D\; 1}}{{t\; 2} - {t\; 1}}.}} & (1)\end{matrix}$

Determination of the readout offset from the linear relationship 260, aswell as other possible ways of collecting the first and second datapoints 262 a, 262 b are described below in greater detail.

In the dark current determination method described above in reference toFIGS. 6A and 6B, the pixel output includes the dark currentcontribution, readout offset contribution, and the spurious chargecontribution. The spurious charge is included because the collectedcharge from the pixel needs to be read out by the shifting operation asdescribed above. For a given pixel, however, the spurious chargecontribution can be approximated as being generally constant for a givenshift-out gate voltage application scheme, and not displayingsignificant dependence on the exposure time. The readout offset can alsobe approximated as being generally constant for the given pixel, and notdepending on the exposure time. The change in the dark current per unitexposure time, as expressed by Equation 1, relates to a difference intwo values of pixel outputs D2 and D1. Thus, the spurious charge andreadout offset contributions can be subtracted out in the dark currentdetermination.

FIGS. 7A and 7B illustrate one possible way of determining the readoutoffset contribution associated with readout from the output register222. FIG. 7A illustrates a readout configuration 270 where the pixelarray 220 is not shifted out, and the readout register 222 is read out(as depicted by an arrow 272) after being reset. In one embodiment, anoutput from such a readout includes the readout offset and does notinclude the spurious charge from the main array and dark currentcontributions associated with the main array pixels.

FIG. 7B illustrates a process 280 that performs such readout offsetdetermination. The process 280 begins at a start state 282, and in step284 that follows, the process 280 resets the output register 222. Instep 286 that follows, the process 280 reads out the output register222. In step 290 that follows, the process 280 determines the readoutcontribution resulting from the readout of the output register 222. Theprocess 280 ends at a stop state 292.

FIG. 8A illustrates a shift-and-readout configuration 300 that issimilar to what might occur during a data-acquiring process. To allowdark current determination and correction, a substantially darkenvironment can be provided for the pixel array 220. The pixel array 220is shifted out as depicted by arrows 228 a-228 d, and following eachshifting operation, the output register 222 is read out as depicted byan arrow 302. The output register 222 is typically reset prior toreceiving the shift of charges from the pixel row (218 in FIG. 4).

FIG. 8B illustrates a process 310 that performs such a shift and readoutoperations described above in reference to FIG. 8A. The process 310begins at a start state 312, and in step 314 that follows, the process310 induces exposure (integration) by the pixels for an exposure time t.In step 316 that follows, the process 310 resets the output register 222and performs the shift operation such that the output register 222receives a row of charges. In step 320 that follows, the output register222 is read out. Steps 316 and 320 are repeated as needed until all ofthe rows are shifted out and read out. In step 322 that follows, theprocess 310 removes the dark current and readout offset contributionsfrom the pixel outputs to yield the spurious charge contribution. Theprocess 310 ends at a stop state 324.

The pixel outputs provided in the foregoing manner includes thecontributions from the spurious charge, dark current, and the readoutoffset. The dark current contribution can be approximated by the methoddescribed above in reference to FIGS. 6A and 6B. That is, the darkcurrent associated with the exposure time t can be approximated as(ΔD/Δt)t, where ΔD/Δt is expressed in Equation 1. The readout offsetcontribution can be approximated by the method described above inreference to FIGS. 7A and 7B. Since the readout offset contributiongenerally does not depend on the exposure time t, a previouslydetermined readout offset can be used.

FIGS. 9A and 9B illustrate an alternate method for determining the noisecontributions. As previously described in reference to FIGS. 6A and 6B,two data points provided at two different exposure times can be used toapproximate the dark current. In certain configurations of the CCD, thenumber of charged particles generated as dark current can be relativelysmall. As is understood in the art, random events such as the darkcurrent generation are subject to Poisson fluctuation, where theuncertainty in the expected number N of generated particles isapproximately 1/√{square root over (N)}. Thus as an example, if anaverage of 100 charged particles are produced during a given exposureduration, N=100 and the uncertainty is 1/10=0.1 (10%). If the expectednumber N=10, the uncertainty jumps to approximately 33%. Thus, the twoexemplary data points 262 a and 262 b of FIG. 6B can have significantuncertainties. As a result, the dark current determined can also have asignificant uncertainty associated with it.

In some embodiments, a detector can include a masked region for darkcurrent determination. Measurements from the masked region can beprovided concurrently with data measurements from the main array. Thedark current contribution can be determined by fitting a linear curve ina manner described below.

One possible way to mitigate the Poisson fluctuation is to perform thesame measurement sufficient number of times to accurately determine theexpected value of the dark current for a given exposure time. Theplurality of measured dark current values then yields a Gaussiandistribution whose peak (and width) can be determined in any number ofknown ways. Such a technique can be particularly useful for hot pixelsfor which approximation of dark current from the average background canbe difficult.

FIG. 9A illustrates a process 330 that performs a plurality of darkcurrent measurements to more accurately determine the expected darkcurrent production per exposure time. The process 332 begins at a startstate 332, and in step 334 that follows, the process 330 induces thedetector to be subjected to a substantially dark condition. In step 336that follows, the process 330 provides exposures from the pixels at twoor more exposure times. For the purpose of description herein, twoexposure times are used; however, it will again be appreciated that anynumber greater than two can be used without departing from the spirit ofthe present teachings. In step 340 that follows, the process 340determines if the number of data points provided thus far isstatistically sufficient. One way to determine such an accuracy criteriais described below in reference to FIG. 9B. In a decision step 342 thatfollows, the process 330 determines if the collected data is sufficient.If the collected data is not sufficient, the process 330 loops back tostep 336 to collect more data. If the collected data is sufficient, theprocess 330 in step 344 processes the collected pixel output data todetermine the dark current contribution. The process 330 in step 346then can determine other noise contributions based on the dark currentcontribution. The process 330 ends in a stop state 348.

In one implementation of the process 330, the repetitive loop from thedecision step 342 to step 336 can be performed so as to collect M setsof the two data points. The M sets can be provided one set (of two datapoints at two different exposure times) at a time. Alternatively, thefirst data point at the first exposure time can be provided M times,followed by M second data points at the second exposure time. In someembodiments, the latter method is less susceptible to variations intemperature.

The collection of the plurality of data points in the foregoing mannercan yield a pixel output (S) dependence 350 on the exposure time (t). Acluster of first data points 352 a corresponds to the exposure time t1,and a cluster of second data points 352 b corresponds to the exposuretime t2. The clusters of first and second data points 352 a, 352 b canbe projected onto the “S” axis to form Gaussian distributions. Meanvalues of the first and second Gaussians can be determined in any numberof known ways. The first mean value S1 can then correspond to the firstexposure time t1, and the second mean value S2 can correspond to thesecond exposure time t2.

Once the first and second mean data points (t1, S1) and (t2, S2)representative of the first and second clusters of exposure data points,a linear relationship 354 can be provided. The line 354 extends throughthe first and second mean data points and can extend beyond so as toallow extrapolation of the pixel output S for an arbitrary exposure timet. The slope of the line 354, ΔS/Δt=(S2−S1)/(t2−t1), represents the darkcurrent per unit exposure time in a similar manner as that of Equation 1described above in reference to FIG. 6B. Thus, a dark currentcorresponding to a given exposure time t can be determined as (ΔS/Δt)t.In one embodiment, hot pixels are determined and excluded from theforegoing approach.

The pixel output curve 350 described above in reference to FIG. 9B canreflect an extension of the pixel output curve 260 also described abovein reference to FIG. 6B. The repetition of pixel output measurements Mtimes in the foregoing manner provides an improved approximation of thefirst and second pixel outputs by mitigating the random fluctuations. Itwill be appreciated that similar repetitive-measurement counterpart tothe readout offset determination of FIGS. 7A and 7B can be performed toprovide an improved mean value of the readout offset contribution.

It will be understood that the readout offset contribution provided inthe foregoing manner can be applied globally to each of the pixels. Theexemplary pixel output curve 350 is representative of a selected pixel.Thus, each pixel can have associated with it information that correlatesthe exposure time t to that pixel's output S. Such information caninclude the slope and y-intercept of a linear relationship between S andt.

As previously described, the slope of the S-t relationship representsthe dark current per unit exposure time. In one embodiment, they-intercept (S0 in FIG. 9B) should represent a substantially nilexposure time; thus, S0 includes the spurious charge and readout offsetcontributions but not the dark current contribution. Subtraction of theglobal readout offset value from the S0 value yields the pixel'sspurious charge contribution. Many cameras, however, have a non-zerotime intercept; thus, a nominal origin of the S-t curve can be greaterthan or less than zero. Such an origin can be determined by plottingseveral different curves associated with different light exposureintensities as a function of time. A crossing point or a generalcrossing area can represent the origin.

As illustrated in FIG. 9B, an alternative method of approximating thevalue of S0 (spurious charge plus readout offset values) includesproviding of the given pixel's output S0′ at a relatively very shortexposure duration of t0′. In some embodiments, because of the relativelysmall value of t0′, the corresponding dark current contribution can beneglected, and the value of S0′ includes the spurious charge and readoutoffset contributions. Subtraction of the global readout offset valuefrom the S0′ value can then yield an approximation of the pixel'sspurious charge contribution. In certain circumstances, such a methodcan be advantageous because the method does not need to depend on they-intercept extrapolated from the first and second data points 352 a andb.

In some embodiments, such as in uncooled cameras, the dark current fromreadout is not negligible. Furthermore, when clocking, the dark currentcan increase particularly when operated in an MPP mode.

FIG. 9C illustrate how mean values of the plurality of pixel outputs canbe determined so as to yield the S1 and S2 values. As previouslydescribed, projection of the pixel output data points onto the “S” axisforms a distribution of the pixel output values. Such a distribution 600is shown in FIG. 9C, where the pixel output values are histogrammed intoa plurality of appropriately sized bins 602 so as to yield first andsecond distributions 604 and 606 corresponding to the first and secondclusters of data points.

As shown in FIG. 9C, exemplary Gaussian analyses 610 and 612 can be bitto the respective distributions 604 and 606. Gaussian analysis to fitthe curve to the distributions can be performed in any number of knownways. Furthermore, one can provide mean values S1 and S2 based on theGaussian analyses 610 and 612.

FIG. 9D illustrates a similar distribution 620 for the plurality ofreadout offset values provided in a manner as described above. Thereadout offset values can be histogrammed into a plurality ofappropriately sized bins 622 so as to yield a readout offset histogram624. The histogram 624 can then be fit with a Gaussian analysis 626 soas to allow determination of a mean offset value that can represent thereadout offset contribution.

FIGS. 10A and B now illustrate an exemplary method of characterizing thespurious charge contribution while varying the gate voltage profile toprovide a gate voltage profile that results in a desired level ofspurious charge. FIG. 10A illustrates an exemplary transferconfiguration 360 having a potential profile 364. The exemplarypotential profile 364 includes the well underneath the gate 186 b, andan inverted region 372 underneath the gate 186 c. The inverted region372 has a potential depth 370 that is caused by the application of agate voltage on the gate 186 c. In general, the potential depth 370determines, at least to some degree, the extent of the spurious chargeproduction. The spurious charge production can also depend on the mannerin which the potential transitions from the non-inverted to invertedconfiguration (i.e., transition of the gate voltage at the gate 186 c).In some embodiments, the spurious charge production depends particularlyon the speed of transition of the clock edge. The gate voltagestypically need to be larger for a higher clock speed to maintain asimilar well depth. In FIG. 10A, a potential transition region 366depicts the transition from the collection well to the inverted region372. The spurious charge production can also depend on how long theinverting gate voltage is applied.

In certain embodiments, the various possible manipulation of thepotential profile 364 can be induced by the controller 170. Thecontroller 170 can control the manner in which a gate voltage circuit362 applies the various gate voltages.

FIG. 10B illustrates an exemplary process 380 that characterizes thespurious charge for various configurations of the gate voltageapplication. The process 380 begins at a start state 382, and in step384 that follows, the process 380 determines a desired range of spuriouscharge associated with a CCD being utilized for a given biologicalanalysis. In step 386 that follows, the process 380 induces a variationof one or more gate voltage parameter so as to cause one or more of thepotential profile change(s) described above in reference to FIG. 10A. Instep 390 that follows, the process determines the spurious chargeresulting from the potential profile selected. In step 392 that follows,the process 380 determines whether the spurious charge contribution iswithin the desired range. In a decision step 394 that follows, theprocess 380 determines whether the variations to the gate voltageparameter(s) should continue. If the answer is “Yes,” the process 380loops back to step 386 so as to facilitate additional gate voltagevariation(s). If the answer is “No,” the process 380 proceeds to step396 where the process 380 causes the gate voltage parameter(s) to besaved and/or implemented. The process 380 ends at a stop state 400.

It will be appreciated that a gate voltage parameter “tuning” processcan be used to map out a spurious charge response to one or more of theparameters. In certain embodiments, the pixels in the array can responddifferently to a given set of parameters. One way to optimize themanagement of spurious charge is to select a set of parameters thatresults in the least amount of average spurious charge from all of thepixels. It will be appreciated that one can apply the gate voltageparameters determined in the foregoing manner in any number of ways toany combination of pixels without departing from the spirit of thepresent teachings.

Being able to characterize the pixel's noise contributions in theforegoing manner allows one to characterize the pixel array in animproved manner. FIGS. 11 to 13 now illustrate some exemplaryapplications that can benefit from the knowledge and availability of thepixel noise contributions. In particular, FIGS. 11A-11C illustrate how“hot” pixels can be identified in the pixel array. FIGS. 12A-12Cillustrate how “cold” or “dead” pixels can be identified in the pixelarray. FIGS. 13A-13C illustrate how the pixels' responses can benormalized. As is known in the art, a hot pixel can manifest anexcessive dark current generation. A dead pixel can manifest a lack ofoutput or dark current in response to an impinging signal. Pixelstypically manifest some variations in the level of output for asubstantially same input and under substantially same operatingcondition.

FIG. 11A illustrates a process 410 that determines the dark currentcontribution for each of the pixels in the array. The process 410 beginsat a start state 412, and in step 414 that follows, the process 410induces a dark condition to be provided for the detector. In step 416that follows, the process 410 determines the dark current contributionfor each pixel of the array. The dark current contribution determinationcan include removal of the spurious charge and readout offsetcontributions from the pixels' output in a manner described above. Theprocess 410 ends at a stop state 420.

In certain embodiments, the process 410 can have been performedpreviously, and the dark current contribution for the pixels can alreadybe stored in some database. In other embodiments, some or all of thedark current contribution determination can be performed for the purposeof hot pixel identification.

FIG. 11B illustrates a process 430 that uses the pixels' dark currentinformation to identify the hot pixels. The process 430 begins at astart state 432, and in step 434 that follows, the process 430determines the average value of the dark current contribution of thepixels. In certain implementations of the process 430, dark currentvalue(s) that deviate substantially from the general cluster of“mainstream” values can be removed from the averaging process via anynumber of known techniques such as using a median or weighted average(averaging those values not excluded by more than a given standarddeviation). In step 436 that follows, the process 430 determines athreshold value of the dark current contribution, above which a pixelcan be considered to be hot. In step 440 that follows, the process 430compares the dark current of each pixel to the threshold value toidentify the hot pixels. The process 430 ends at a stop state 442.

FIG. 11C illustrates an exemplary dark current distribution 450 thatdepicts a plurality pixels 452 and their corresponding dark currents454. An exemplary threshold level 456 is also indicated as a dashedline. In such an exemplary pixel array, one can see that pixel 452 emanifests a dark current that exceeds the threshold level 456. Thus, theexemplary pixel 452 e can be identified as a hot pixel.

In certain embodiments, the threshold level can be set at approximatelythree standard deviations above a noise level. Many images can then beused to insure that pixels which are not hot are not inadvertentlyselected. It will be appreciated, however, that the threshold level canbe set at any level without departing from the spirit of the presentteachings.

FIG. 12A illustrates a process 460 that determines the pixels' responseto impinging light signal so as to facilitate identification of deadpixels. The process 460 begins at a start state 462, and in step 464that follows, the process 460 induces a substantially uniformillumination condition to be provided for the detector. In step 466 thatfollows, the process 460 provides an exposure from the detector for aselected exposure duration. Such an exposure includes exposures from theindividual pixels. In step 470 that follows, the process 460 removes thedark current, spurious charge, and readout offset contributions fromeach pixel's output signal to yield each pixel's corrected signal. Theprocess 460 ends at a stop state 472.

In certain embodiments, the process 460 can have been performedpreviously, and the corrected signals for the pixels can already bestored in some database. In other embodiments, some or all of thecorrected signal determination can be performed for the purpose of deadpixel identification.

FIG. 12B illustrates a process 480 that uses the pixels' correctedsignal information to identify the dead pixels. The process 480 beginsat a start state 482, and in step 484 that follows, the process 480determines the average value of the corrected signals of the pixels. Incertain implementations of the process 480, corrected signal values thatdeviate substantially from the general cluster of “mainstream” valuescan be removed from the averaging process via any number of knowntechniques. In step 486 that follows, the process 480 determines athreshold value of the corrected signal, below which a pixel can beconsidered to be dead or cold. In step 490 that follows, the process 480compares the corrected signal of each pixel to the threshold value toidentify the dead/cold pixels. The process 480 ends at a stop state 492.

FIG. 12C illustrates an exemplary corrected signal distribution 500 thatdepicts the plurality pixels 452 and their corresponding correctedsignals 502. An exemplary threshold level 504 is also indicated as adashed line. In such an exemplary pixel array, one can see that pixel452 g manifests an exemplary corrected signal that is substantially niland therefore falls below the threshold level 504. Thus, the exemplarypixel 452 g can be identified as a dead pixel. In certain embodiments,pixels whose corrected signals are finite but fall below the threshold504, can be identified as cold pixels.

In certain embodiments, the threshold level can be set in a mannergenerally similar to that for hot pixels so as to reduce the likelihoodthat non-cold pixels are not inadvertently selected. It will beappreciated, however, that the threshold level can be set at any levelwithout departing from the spirit of the present teachings.

FIG. 13A illustrates a process 510 that determines the pixels' responseto a substantially uniform impinging light signal so as to facilitatethe comparison of the pixels' response. This signal noise contributionassociated with how a pixel responds to substantially uniform light isknown as photo response non-uniformity. The process 510 begins at astart state 512, and in step 514 that follows, the process 510 induces asubstantially uniform illumination condition to be provided for thedetector. In step 516 that follows, the process 510 provides an exposurefrom the detector for a relatively long exposure duration. Such anexposure includes exposures from the individual pixels. In step 520 thatfollows, the process 510 removes the dark current, spurious charge, andreadout offset contributions from each pixel's output signal to yieldeach pixel's corrected signal. The process 510 ends at a stop state 522.

In certain embodiments, the process 510 can have been performedpreviously, and the corrected signals for the pixels can already bestored in some database. In other embodiments, some or all of thecorrected signal determination can be performed for the purpose of pixelresponse analysis described below.

FIG. 13B illustrates a process 530 that uses the pixels' correctedsignal information to compare the pixels' responses to a substantiallyuniform input. The process 530 begins at a start state 532, and in step534 that follows, the process 530 determines the average value of thecorrected signals of the pixels that are not hot or dead. In step 536that follows, the process 530 determines a normalization factor for eachpixel based on the average value of the corrected signals. In step 540that follows, the process 530 saves the normalization factors for thepixels to allow application during subsequent measurement of signals.The process 530 ends at a stop state 542.

FIG. 13C illustrates an exemplary corrected signal distribution 550 thatdepicts the plurality pixels 452 and their corresponding correctedsignals 552. An exemplary average value 554 of the corrected signals isalso indicated as a dashed line. In such an exemplary pixel array, onecan see that the exemplary hot pixel 452 e is identified as being hot,and its output signal is removed from the distribution. Furthermore, theexemplary dead pixel 452 g is identified as a being dead. The remainingexemplary pixels 452 a-452 d, 452 f, and 452 h-452 j have associatedwith them normalization factors. For example, the pixel 452 a has anexemplary normalization factor of 1.2, meaning that in this particularexample of normalization scheme, the pixel's output is approximately 20%above the average response level. Thus, to normalize a subsequentcorrected signal from pixel 452 a that corrected signal can be dividedby the factor 1.2 thereby reducing the value of the signal byapproximately 20%. It will be appreciated that any number ofnormalization schemes can be utilized to normalize the pixels' responsewithout departing from the spirit of the present teachings.

FIG. 14 now illustrates an exemplary processing of signals to correctfor the noise contributions described above, thereby improving thequality of the measurement. An exemplary fiber array 560 having aplurality of tips 562 is used for descriptive purpose. It will beappreciated, however, that the correction process can be applied to anyother biological analysis systems without departing from the spirit ofthe present teachings.

An exemplary cluster 564 of fragments is depicted as being attached toone of the fiber tips 562. The fragments 564 are tagged with fluorescinglabels that emit detectable signal 566 whose intensity and spatialdistribution are indicative of the type and concentration of thefragment in the sample. The signal 566 impinges on the detector, and thepixels are read out by shifting and register readout operations so as toyield a raw data 570 including output signals 572 corresponding to thepixels 452.

The raw data 570 can then be corrected for the noise contributions asdescribed above. An offset information 574 is depicted as including thenet correction value which includes the readout offset, spurious charge,and dark current contributions. A correction value is associated witheach pixel, and preferably the dark current contribution accounts forthe exposure duration of the shot of the sample signal 566. The offsetinformation 574 can also identify the hot and/or dead pixels determinedas described above. Although the offset information 574 is depicted as asingle entity in FIG. 14, it will be appreciated that the noisecontributions information as well as the hot/dead pixel list can bestored in some form of a database in any number of ways.

Removal of the offset values from the pixels' raw data yields acorrected data 576 including corrected signals 580 corresponding to thepixels 452. The corrected data 576 is also depicted as having signals(or lack of signals) associated with the hot and dead pixels removedfrom further processing.

The corrected data 576 can be normalized by incorporating thenormalization factors as depicted by a normalization factor information582. The information 582 can be stored in some form of a database in asimilar manner as that of the offset information 574 described above.Normalization of the corrected data 576 yields a normalized data 584including normalized signals 586 corresponding to the pixels 452.

At this stage, the normalized data 584 is a more accurate representationof the detected signal 566 than that of the raw data 570 or thecorrected data 576. In certain embodiments, the gaps resulting from thehot and/or dead pixels can be accounted for in any number of ways. Forexample, the hot pixel 452 e can be assigned an approximated value bytaking an average of the signal values of the two neighboring pixels.Thus, the approximated value e for pixel 452 e can be expressed as(d+f)/2. Similarly, the dead pixel 452 g can be assigned an approximatedvalue g=(f+h)/2. Similarly in a 2-dimensional image, eight surroundingpixels can be averaged to approximate a value for the hot/dead pixel.

The correction, normalization, and possibly gap filling, as describedabove, yields analysis data 590 that is representative of the samplesignal 566. The analysis data 590 includes analysis signals 592corresponding to the pixels 452. The analysis data 590 can further becharacterized by a fit curve 594 so as to allow parameterizing the data590 in terms of spatial distribution and/or the intensity of the samplesignal 566.

It will be appreciated that the various noise contributions associatedwith the operation of the CCD can be determined, stored, and applied inany combination. The application of the noise contributions can includethe correction of the measured data as described above to yield animproved representation of the biological sample being analyzed. Theapplication of the noise contribution can also include characterizationof the various biological analysis devices, some of which wereexemplified above, for the purpose of machine diagnostics and/orcalibration. Furthermore, the various noise contributions correctionparameters can be provided during various calibration stages of thedevices, in conjunction with the sample measurements, or any combinationthereof, without departing from the spirit of the present teachings.

Although the above-disclosed embodiments of the present invention haveshown, described, and pointed out the fundamental novel features of theinvention as applied to the above-disclosed embodiments, it should beunderstood that various omissions, substitutions, and changes in theform of the detail of the devices, systems, and/or methods illustratedcan be made by those skilled in the art without departing from the scopeof the present invention. Consequently, the scope of the inventionshould not be limited to the foregoing description, but should bedefined by the appended claims.

1. A method for a biological system, comprising: providing aphotodetector comprising a plurality of pixels capable of forming anoptical image of fluorescent light emitted from a biological sample;exposing the photodetector to a test signal from a biological sample;providing pixel noise data for a plurality of pixels of thephotodetector; generating a response of the plurality of pixels to auniform input signal; based on the response, determining correctedsignals data for at least some of the pixels from the plurality ofpixels.
 2. The method of claim 1, further comprising: determining anaverage based on the corrected signals data; and determining anormalization factor for at least some of the pixels of the plurality ofpixels based at least in part on the average.
 3. The method of claim 2,further comprising saving the normalization factors for the pixels forapplication on measurement signals produced by the pixels of thephotodetector.
 4. The method of claim 3, further comprising: correctingthe test signal using the corrected signals data.
 5. The method of claim4, further comprising determining a signal value of the one or morepixels based on the corrected test signal, wherein the one or morepixels comprises at least one of a dead pixel or a hot pixel.
 6. Themethod of claim 1, further comprising storing the corrected signals datacomprises data in a database.
 7. The method of claim 1, whereinproviding pixel noise data comprises one or more of providing a darknoise characterization for at least some of the pixels from theplurality of pixels, providing spurious charge noise for at least someof the pixels from the plurality of pixels, and providing readout offsetnoise for at least some of the pixels from the plurality of pixels. 8.An imaging system for analysis of a biological sample, comprising: asample region configured to contain a plurality of biological samplescomprising a labeled fragment; a photodetector comprising a plurality ofpixels; optics configured to transfer energy emission from thebiological samples to the photodetector; a data structure containingpixel noise data for at least some of the plurality of pixels andcorrected signals data, the corrected signals data comprising acomparison of a response of the plurality of pixels to a uniform input;and a processor configured to adjust measurement signals produced by thephotodetector based at least in part on the pixel noise data and thecorrected signals data.
 9. The imaging system of claim 8, wherein thedata structure further comprises one or more of: an average of thecorrected signals data; or a normalization factor for at least some ofthe pixels of the plurality of pixels, the normalization factor beingbased at least in part on the average.
 10. The imaging system of claim8, wherein the data structure further comprises a normalization factorfor at least some of the pixels of the plurality of pixels based atleast in part on an average of the corrected signals data.
 11. Theimaging system of claim 8, further comprising a controller configured tocorrect signal noise from measurement signals produced by thephotodetector, wherein signal noise comprises at least one of a darkcurrent contribution or a readout offset contribution.
 12. The imagingsystem of claim 8, wherein the processor is configured to determine oneor more of a dark current contribution and a readout offset contributionfrom measurement signals produced by the photodetector.
 13. The imagingsystem of claim 8, wherein the photodetector is a CCD detector or a CMOSdetector.
 14. The imaging system of claim 8, wherein the labeledfragment comprises a reaction component of an amplification reaction.15. The imaging system of claim 8, wherein the labeled fragmentcomprises a reaction component of a sequencing reaction.
 16. The methodof claim 1, further comprising providing a labeled fragment in thebiological sample and performing an amplification reaction.
 17. Themethod of claim 1, further comprising providing a labeled fragment inthe biological sample and performing a sequencing reaction.
 18. Animaging system for analysis of a biological sample, comprising: aphotodetector comprising a plurality of pixels; a data structurecontaining pixel noise data for at least some of the plurality of pixelsand corrected signals data, the corrected signals data comprising acomparison of a response of the plurality of pixels to a uniform input;and a processor configured to adjust measurement signals produced by thephotodetector based at least in part on the pixel noise data and thecorrected signals data; wherein the data structure further comprises oneor more of: an average of the corrected signals data; or a normalizationfactor for at least some of the pixels of the plurality of pixels, thenormalization factor being based at least in part on the average.